We adopt the Email Campaign data as used in previous works [1]}. The dataset is constructed based on the online advertising process. Its goal is to determine the best advertisement recommendation strategy for diverse user groups to improve their click through ratio (CTR), thus optimize the revenue generated through advertisements.
Figure REF  shows the topology of the causal graph. We use \(X_1\) , \(X_2\) , \(X_3\)  to denote three user profile attributes, gender, age and occupation; \(A_1\) , \(A_2\) , \(A_3\)  to denote three arm features, product, purpose, send-time that could be intervened; \(I_1\) , \(I_2\) , \(I_3\) , \(I_4\)  to denote Email body template, fitness, subject length, and user query; and \(R\)  to denote the reward that indicates whether users click the advertisement. The reward function is \(R = 1/12(I_1 + I_2 + I_3 + A_3) + \mathcal {N}(0,\sigma ^2)\) , where \(\sigma = 0.1\) .
In our experiment, we set \(\delta = 1/t^2\)  for each \(t \in [T]\) . In Appendix E.1, we show the domain values of all 11 attributes and their conditional probability tables.
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